Zobrazeno 1 - 10
of 857
pro vyhledávání: '"Morlier, P."'
Autor:
Catalani, Giovanni, Agarwal, Siddhant, Bertrand, Xavier, Tost, Frederic, Bauerheim, Michael, Morlier, Joseph
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for differe
Externí odkaz:
http://arxiv.org/abs/2407.19916
Autor:
Ribeiro, Lucas Grativol, Gauthier, Lubin, Leonardon, Mathieu, Morlier, Jérémy, Lavrard-Meyer, Antoine, Muller, Guillaume, Fresse, Virginie, Arzel, Matthieu
Publikováno v:
ISCAS 2024 : IEEE International Symposium on Circuits and Systems, May 2024, Singapore, Singapore
This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, especially when the costs of data acquisition or labeling prove to be proh
Externí odkaz:
http://arxiv.org/abs/2404.19354
Publikováno v:
Struct Multidisc Optim 67, 81 (2024)
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involv
Externí odkaz:
http://arxiv.org/abs/2311.06130
Autor:
Giovanni Catalani, Siddhant Agarwal, Xavier Bertrand, Frédéric Tost, Michael Bauerheim, Joseph Morlier
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains fo
Externí odkaz:
https://doaj.org/article/2eaa705ddbeb4386b1ccd63972513e5c
Autor:
Saves, Paul, Lafage, Remi, Bartoli, Nathalie, Diouane, Youssef, Bussemaker, Jasper, Lefebvre, Thierry, Hwang, John T., Morlier, Joseph, Martins, Joaquim R. R. A.
Publikováno v:
Advances in Engineering Software Volume 188, February 2024, 103571
The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces s
Externí odkaz:
http://arxiv.org/abs/2305.13998
Autor:
Martin, Pierre-Etienne, Calandre, Jordan, Mansencal, Boris, Benois-Pineau, Jenny, Péteri, Renaud, Mascarilla, Laurent, Morlier, Julien
Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this t
Externí odkaz:
http://arxiv.org/abs/2301.13576
Selecting the optimal material for a part designed through topology optimization is a complex problem. The shape and properties of the Pareto front plays an important role in this selection. In this paper we show that the compliance-volume fraction P
Externí odkaz:
http://arxiv.org/abs/2211.15358
Publikováno v:
Neurocomputing (2023)
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxa
Externí odkaz:
http://arxiv.org/abs/2211.08262
Multidisciplinary Design Optimization (MDO) makes it possible to reach a better solution than by optimizing each discipline independently. In particular, the optimal structure of a drone won't be the same depending on the material used. The CO2 footp
Externí odkaz:
http://arxiv.org/abs/2208.13710
In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, materials and cross-section type. The proposed methodology consists of usi
Externí odkaz:
http://arxiv.org/abs/2207.05314